When: Sep 1st, 2021 – 11:00 – 11:45 AM
Where: Google meet link
By: Edison Ong, Haihe Wang, Mei U Wong, Meenakshi Seetharaman, Ninotchka Valdez and Yongqun He.
Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction.However, ML-based RV still faces challenges in prediction accuracy and program accessibility. This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physio-chemical features extracted from well-defined training data. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, andone epitope-based method using a high-quality benchmark dataset, showing superior performance in predicting BPAgs.